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Article: Predicting crash frequency using an optimised radial basis function neural network model

TitlePredicting crash frequency using an optimised radial basis function neural network model
Authors
Issue Date2016
PublisherTaylor & Francis. The Journal's web site is located at http://www.tandfonline.com/loi/ttra21
Citation
Transportmetrica A: Transport Science, 2016, v. 12, p. 330-345 How to Cite?
Persistent Identifierhttp://hdl.handle.net/10722/223845
ISSN
2015 Impact Factor: 1.477
2015 SCImago Journal Rankings: 1.352

 

DC FieldValueLanguage
dc.contributor.authorHuang, H-
dc.contributor.authorZeng, Q-
dc.contributor.authorPei, X-
dc.contributor.authorWong, SC-
dc.contributor.authorXu, P-
dc.date.accessioned2016-03-18T02:29:54Z-
dc.date.available2016-03-18T02:29:54Z-
dc.date.issued2016-
dc.identifier.citationTransportmetrica A: Transport Science, 2016, v. 12, p. 330-345-
dc.identifier.issn2324-9935-
dc.identifier.urihttp://hdl.handle.net/10722/223845-
dc.languageeng-
dc.publisherTaylor & Francis. The Journal's web site is located at http://www.tandfonline.com/loi/ttra21-
dc.relation.ispartofTransportmetrica A: Transport Science-
dc.titlePredicting crash frequency using an optimised radial basis function neural network model-
dc.typeArticle-
dc.identifier.emailWong, SC: hhecwsc@hku.hk-
dc.identifier.authorityWong, SC=rp00191-
dc.identifier.doi10.1080/23249935.2015.1136008-
dc.identifier.hkuros257244-
dc.identifier.volume12-
dc.identifier.spage330-
dc.identifier.epage345-
dc.publisher.placeUnited Kingdom-

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